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Monitoring Depth of Anesthesia

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Computational Intelligence Processing in Medical Diagnosis

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 96))

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Abstract

This chapter examines the use of complexity analysis, approximate entropy, wavelet transforms, artificial neural networks, fuzzy logic, and neuro-fuzzy method (adaptive network-based fuzzy inference systems) to determine the depth of anesthesia (DOA) of a patient by analyzing mid-latency auditory evoked potentials (MLAEP) and electroencephalograms (EEG). Comparisons are made of the success and computational efficiency of each technique using the data of experimental dogs with different anesthetic modalities.

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Authors
  1. J. W. Huang
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  2. X.-S. Zhang
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  3. R. J. Roy
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Editor information

Editors and Affiliations

  1. Technical University of Munich, Ismaninger Straße 22, 81675, München, Germany

    Manfred Schmitt

  2. Romanian Academy, Calea Victoriei 125, Bucharest, Romania

    Horia-Nicolai Teodorescu

  3. The Queens Elizabeth Hospital, Woodville Road, 5011, Woodville, Adelaide, South Australia, Australia

    Ashlesha Jain

  4. Bellevue Residential Care Centre, 5050, Bellevue Heights, Adelaide, South Australia, Australia

    Ajita Jain

  5. Julia Farr Services, Fullarton Road, 5063, Adelaide, South Australia, Australia

    Sandyha Jain

  6. Knowledge-Based Intelligent, Engineering Systems Centre, University of South Australia, 5095, Mawson Lakes, Adelaide, South Australia, Australia

    Lakhmi C. Jain

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© 2002 Springer-Verlag Berlin Heidelberg

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Huang, J.W., Zhang, XS., Roy, R.J. (2002). Monitoring Depth of Anesthesia. In: Schmitt, M., Teodorescu, HN., Jain, A., Jain, A., Jain, S., Jain, L.C. (eds) Computational Intelligence Processing in Medical Diagnosis. Studies in Fuzziness and Soft Computing, vol 96. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1788-1_13

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  • DOI: https://doi.org/10.1007/978-3-7908-1788-1_13

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-2509-1

  • Online ISBN: 978-3-7908-1788-1

  • eBook Packages: Springer Book Archive

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